Reducing Bias and Quantifying Uncertainty in Fluorescence Produced by PCR.

We present a new approach for relating nucleic-acid content to fluorescence in a real-time Polymerase Chain Reaction (PCR) assay. By coupling a two-type branching process for PCR with a fluorescence analog of Beer's Law, the approach reduces bias and quantifies uncertainty in fluorescence. As the two-type branching process distinguishes between complementary strands of DNA, it allows for a stoichiometric description of reactions between fluorescent probes and DNA and can capture the initial conditions encountered in assays targeting RNA. Analysis of the expected copy-number identifies additional dynamics that occur at short times (or, equivalently, low cycle numbers), while investigation of the variance reveals the contributions from liquid volume transfer, imperfect amplification, and strand-specific amplification (i.e., if one strand is synthesized more efficiently than its complement). Linking the branching process to fluorescence by the Beer's Law analog allows for an a priori description of background fluorescence. It also enables uncertainty quantification (UQ) in fluorescence which, in turn, leads to analytical relationships between amplification efficiency (probability) and limit of detection. This work sets the stage for UQ-PCR, where both the input copy-number and its uncertainty are quantified from fluorescence kinetics.

Binding, brightness, or noise? Extracting temperature-dependent properties of dye bound to DNA.

We present a method for extracting temperature-dependent thermodynamic and photophysical properties of SYTO-13 dye bound to DNA from fluorescence measurements. Together, mathematical modeling, control experiments, and numerical optimization enable dye binding strength, dye brightness, and experimental noise (or error) to be discriminated from one another. By focusing on the low-dye-coverage regime, the model avoids bias and can simplify quantification. Utilizing the temperature-cycling capabilities and multi-reaction chambers of a real-time PCR machine increases throughput. Significant well-to-well and plate-to-plate variation is quantified by using total least squares to account for error in both fluorescence and nominal dye concentration. Properties computed independently for single-stranded DNA and double-stranded DNA by numerical optimization are consistent with intuition and explain the advantageous performance of SYTO-13 in high-resolution melting and real-time PCR assays. Distinguishing between binding, brightness, and noise also clarifies the mechanism for the increased fluorescence of dye in a solution of double-stranded DNA compared to single-stranded DNA; in fact, the explanation changes with temperature.

Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning.

Adsorptive hydrogen storage is a desirable technology for fuel cell vehicles, and efficiently identifying the optimal storage temperature requires modeling hydrogen loading as a continuous function of pressure and temperature. Using data obtained from high-throughput Monte Carlo simulations for zeolites, metal-organic frameworks, and hyper-cross-linked polymers, we develop a meta-learning model that jointly predicts the adsorption loading for multiple materials over wide ranges of pressure and temperature. Meta-learning gives higher accuracy and improved generalization compared to fitting a model separately to each material and allows us to identify the optimal hydrogen storage temperature with the highest working capacity for a given pressure difference. Materials with high optimal temperatures are found in close proximity in the fingerprint space and exhibit high isosteric heats of adsorption. Our method and results provide new guidelines toward the design of hydrogen storage materials and a new route to incorporate machine learning into high-throughput materials discovery.

Deep neural network learning of complex binary sorption equilibria from molecular simulation data.

We employed deep neural networks (NNs) as an efficient and intelligent surrogate of molecular simulations for complex sorption equilibria using probabilistic modeling. Canonical (N 1 N 2 VT) Gibbs ensemble Monte Carlo simulations were performed to model a single-stage equilibrium desorptive drying process for (1,4-butanediol or 1,5-pentanediol)/water and 1,5-pentanediol/ethanol from all-silica MFI zeolite and 1,5-pentanediol/water from all-silica LTA zeolite. A multi-task deep NN was trained on the simulation data to predict equilibrium loadings as a function of thermodynamic state variables. The NN accurately reproduces simulation results and is able to obtain a continuous isotherm function. Its predictions can be therefore utilized to facilitate optimization of desorption conditions, which requires a laborious iterative search if undertaken by simulation alone. Furthermore, it learns information about the binary sorption equilibria as hidden layer representations. This allows for application of transfer learning with limited data by fine-tuning a pretrained NN for a different alkanediol/solvent/zeolite system.

Vapor- and Liquid-Phase Adsorption of Alcohol and Water in Silicalite-1 Synthesized in Fluoride Media.

In this work, batch-adsorption experiments and molecular simulations are employed to probe the adsorption of binary mixtures containing ethanol or a linear alkane-1,n-diol solvated in water or ethanol onto silicate-1. Since the batch-adsorption experiments require an additional relationship to determine the amount of solute (and solvent adsorbed, as only the bulk liquid reservoir can be probed directly, molecular simulations are used to provide a relationship between solute and solvent adsorption for input to the experimental bulk measurements. The combination of bulk experimental measurements and simulated solute-solvent relationship yields solvent and solute loadings that are self-consistent with simulation alone, and allow for an assessment of the various assumptions made in literature. At low solution concentrations, the solute loading calculated is independent of the assumption made. At high concentrations, a negligent choice of assumption can lead to systematic overestimation or underestimation of calculated solute loading.

Understanding the unique sorption of alkane-α, ω-diols in silicalite-1.

Adsorption equilibria of alkane-α, ω-diols (propane-1,3-diol, butane-1,4-diol, pentane-1,5-diol, and hexane-1,6-diol) from aqueous solution onto an all-silica zeolite of the type mordenite framework inverted (MFI, also known as silicalite-1) are obtained by simulations and experiments at T = 323 K and also for pentane-1,5-diol (C5) at 348 and 383 K. After an initial slow rise, isotherms at T = 323 K exhibit steep changes in loading, reaching saturation at 10, 9, 8, and 7 molec/uc as the number of carbon atoms of the diols increases from 3 to 6. The abrupt change in loading corresponds to a minimum in the free energy of adsorption (from vapor to zeolite) that is associated with a rapid rise in the number of hydrogen bonds per sorbate molecule due to the formation of large clusters. For C5 at low loading, the centers-of-mass primarily occupy the channel intersections with oxygens oriented along the straight channels where intermolecular hydrogen bonds are formed. At saturation loading, the C5 centers-of-mass instead occupy the straight and zig-zag channels, and nearly all C5 molecules are involved in a percolating hydrogen-bonding network (this also occurs for C6). With increasing temperature, the C5 isotherm decreases in steepness as the minimum in free energy of adsorption decreases in depth and a less-ordered structure of the adsorbed molecules results in a lower number of diol-diol hydrogen bonds. However, the C5 isotherm does not shift significantly in concentration of the adsorption onset, as the free energies of solvation and adsorption increase by similar and compensating amounts. At T = 323 and 348 K, the steep change for the C5 adsorption isotherm is found to be a phase transition (as indicated by a bimodal distribution of unit cell occupancies at intermediate loading) from a less-dense phase with only small hydrogen-bonded clusters to an ordered solid phase with loadings of 8 molec/uc. At T = 383 K, the sorbates are less ordered, the distribution of occupancies becomes unimodal at intermediate loading, and the loading rises more gradually with concentration. Several different enhanced sampling methods are utilized for these simulations.

Comparative Study of the Effect of Defects on Selective Adsorption of Butanol from Butanol/Water Binary Vapor Mixtures in Silicalite-1 Films.

A promising route for sustainable 1-butanol (butanol) production is ABE (acetone, butanol, ethanol) fermentation. However, recovery of the products is challenging because of the low concentrations obtained in the aqueous solution, thus hampering large-scale production of biobutanol. Membrane and adsorbent-based technologies using hydrophobic zeolites are interesting alternatives to traditional separation techniques (e.g., distillation) for energy-efficient separation of butanol from aqueous mixtures. To maximize the butanol over water selectivity of the material, it is important to reduce the number of hydrophilic adsorption sites. This can, for instance, be achieved by reducing the density of lattice defect sites where polar silanol groups are found. The density of silanol defects can be reduced by preparing the zeolite at neutral pH instead of using traditional synthesis solutions with high pH. In this work, binary adsorption of butanol and water in two silicalite-1 films was studied using in situ attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy under equal experimental conditions. One of the films was prepared in fluoride medium, whereas the other one was prepared at high pH using traditional synthesis conditions. The amounts of water and butanol adsorbed from binary vapor mixtures of varying composition were determined at 35 and 50 °C, and the corresponding adsorption selectivities were also obtained. Both samples showed very high selectivities (100-23 000) toward butanol under the conditions studied. The sample having low density of defects, in general, showed ca. a factor 10 times higher butanol selectivity than the sample having a higher density of defects at the same experimental conditions. This difference was due to a much lower adsorption of water in the sample with low density of internal defects. Analysis of molecular simulation trajectories provides insights on the local selectivities in the zeolite channel network and at the film surface.

Ultra-selective high-flux membranes from directly synthesized zeolite nanosheets.

A zeolite with structure type MFI is an aluminosilicate or silicate material that has a three-dimensionally connected pore network, which enables molecular recognition in the size range 0.5-0.6 nm. These micropore dimensions are relevant for many valuable chemical intermediates, and therefore MFI-type zeolites are widely used in the chemical industry as selective catalysts or adsorbents. As with all zeolites, strategies to tailor them for specific applications include controlling their crystal size and shape. Nanometre-thick MFI crystals (nanosheets) have been introduced in pillared and self-pillared (intergrown) architectures, offering improved mass-transfer characteristics for certain adsorption and catalysis applications. Moreover, single (non-intergrown and non-layered) nanosheets have been used to prepare thin membranes that could be used to improve the energy efficiency of separation processes. However, until now, single MFI nanosheets have been prepared using a multi-step approach based on the exfoliation of layered MFI, followed by centrifugation to remove non-exfoliated particles. This top-down method is time-consuming, costly and low-yield and it produces fragmented nanosheets with submicrometre lateral dimensions. Alternatively, direct (bottom-up) synthesis could produce high-aspect-ratio zeolite nanosheets, with improved yield and at lower cost. Here we use a nanocrystal-seeded growth method triggered by a single rotational intergrowth to synthesize high-aspect-ratio MFI nanosheets with a thickness of 5 nanometres (2.5 unit cells). These high-aspect-ratio nanosheets allow the fabrication of thin and defect-free coatings that effectively cover porous substrates. These coatings can be intergrown to produce high-flux and ultra-selective MFI membranes that compare favourably with other MFI membranes prepared from existing MFI materials (such as exfoliated nanosheets or nanocrystals).

A Review of Biorefinery Separations for Bioproduct Production via Thermocatalytic Processing.

With technological advancement of thermocatalytic processes for valorizing renewable biomass carbon, development of effective separation technologies for selective recovery of bioproducts from complex reaction media and their purification becomes essential. The high thermal sensitivity of biomass intermediates and their low volatility and high reactivity, along with the use of dilute solutions, make the bioproducts separations energy intensive and expensive. Novel separation techniques, including solvent extraction in biphasic systems and reactive adsorption using zeolite and carbon sorbents, membranes, and chromatography, have been developed. In parallel with experimental efforts, multiscale simulations have been reported for predicting solvent selection and adsorption separation. We discuss various separations that are potentially valuable to future biorefineries and the factors controlling separation performance. Particular emphasis is given to current gaps and opportunities for future development.

Adsorptive Separation of 1-Butanol from Aqueous Solutions Using MFI- and FER-Type Zeolite Frameworks: A Monte Carlo Study.

Anaerobic fermentation can transform carbohydrates to yield a multicomponent mixture comprising mainly of acetone, 1-butanol, and ethanol (ABE) in a typical weight ratio of 3:6:1. Compared to ethanol, 1-butanol, the main product of ABE fermentation, offers significant advantages as a biofuel or a fuel additive. However, the toxicity of 1-butanol for cell cultures requires broth concentrations to be low in 1-butanol (≈1-2 wt %). An energy-efficient recovery method that performs well even at low 1-butanol concentrations is therefore necessary to ensure economic feasibility of the ABE fermentation process. In this work, configurational-bias Monte Carlo simulations in the Gibbs ensemble are performed to probe the adsorption of 1-butanol/water solutions onto all-siliceous zeolites with the framework types MFI and FER. At low solution concentration, the selectivity and capacity for 1-butanol in MFI are larger than those in FER, while the opposite is true for concentrations at or above those of ABE broths. Structural analysis at various loadings sheds light on the different sorbate-sorbate and sorbate-sorbent interactions that govern trends in adsorption in each zeolite.